Safe RL

RL: Reinforcement Learning

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19 papers in the last four weeks, up 138% on the four weeks before. 0.2% of all new papers.

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Latest papers 153

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Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
May 26, 2026Gengyue Han, Yiheng FengReinforcement Learning Sim-to-Real Transfer
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA
Credibility-Aware Learning and Control for Safe USV Navigation under Perception Uncertainty
May 26, 2026Yuhang Zhang, Shuqi Chai, Yukang Zhang +5Collision Avoidance Robot Navigation
School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China · Shenzhen Research Institute of Big Data, Shenzhen 518172, China · Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China +5
Breaking the Epistemic Trap: Active Perception Under Compound Uncertainty
May 26, 2026Chayan Banerjee, Ethan GoanActive Perception Constrained RL
School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, Australia.
Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
May 26, 2026Dhruv S. Kushwaha, Zoleikha A. BironKoopman Operator Learning Control Barrier Functions
Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning
May 24, 2026Minjae Kwon, Amir Moeini, Shangtong Zhang +1Reinforcement Learning Control Barrier Functions
University of Virginia
Counterfactually Safe Reinforcement Learning
May 24, 2026Jingyi Li, Peng Wu, Chengchun ShiConstrained RL Safe RL
Department of Statistics and Data Science, National University of Singapore · School of Mathematics and Statistics, Beijing Technology and Business University · Department of Statistics, London School of Economics and Political Science
Kernel-Based Safe Exploration in Deep Reinforcement Learning
May 21, 2026Rupak Majumdar, Nikhil Singh, Sadegh SoudjaniControl Barrier Functions RL Exploration
Max Planck Institute for Software Systems, Germany
Implicit Safety Alignment from Crowd Preferences
May 20, 2026Qian Lin, Daniel S. BrownSafe RL Preference-Based RL
Kahlert School of Computing, University of Utah
DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
May 20, 2026Xinyi Wang, Taekyung Kim, Bardh Hoxha +2Control Barrier Functions Risk-Sensitive RL
Department of Robotics, University of Michigan, Ann Arbor, MI, 48109, USA · Toyota Motor North America, Research & Development, Ann Arbor, MI, 48105, USA · Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
May 20, 2026Richa Verma, Bavish Kulur, Sanjay Chawla +1Direct Preference Optimization Constrained RL
TCS Research, Department of CSE, IIT Madras India · Department of Computing Science, University of Alberta Canada · Qatar Computing Research Institute, Hamad Bin Khalifa University Qatar +1
Sampling-Based Safe Reinforcement Learning
May 19, 2026Luca Vignola, Bruce D. Lee, Manish Prajapat +4Robot Safety Safe RL
ETH Zurich
Generative Auto-Bidding with Unified Modeling and Exploration
May 19, 2026Mingming Zhang, Feiqing Zhuang, Na Li +7Automated Bidding Online Advertising
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University · Taobao & Tmall Group of Alibaba
Differentiable Model Predictive Safety for Heterogeneous Mobility at Urban Intersections
May 19, 2026Wenzhe Song, Hao ZhangMulti-Agent Coordination Intelligent Transportation Systems
School of Business Stevens Institute of Technology Hoboken, 07030, USA · Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, 15213, USA
Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
May 18, 2026Prakash Aryan, Kaushik Raghupathruni, Timo Kehrer +1Pedestrian Behavior Modeling Human Behavior Simulation
University of Bern, Bern, Switzerland. · AI4I, The Italian Institute of Artificial Intelligence, Turin, Italy.
Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles
May 16, 2026Jianye Xu, Bassam AlrifaeeReward Shaping Control Barrier Functions
Department of Computer Science, RWTH Aachen University, Germany · Department of Aerospace Engineering, University of the Bundeswehr Munich, Germany
Imperfect World Models are Exploitable
May 15, 2026Logan Mondal Bhamidipaty, Esmeralda S. Whitammer, David Abel +2Reward Hacking World Model-Based Planning
University of Edinburgh · Stanford University
parallelcbf: A composable safety-filter and auditability framework for tensor-parallel reinforcement learning
May 15, 2026Yijun Lu, Zilei Yang, Yuyin MaReinforcement Learning Control Barrier Functions
Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability
May 14, 2026Yushen Liu, Yin-Jen Chen, Ziyi Chen +4Safety-Critical Control Partially Observable RL
University of Virginia · Google · University of Maryland, College Park +3
Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints
May 13, 2026Timofey TomashevskiyNon-Stationary RL Reinforcement Learning
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
From Cumulative Constraints to Adaptive Runtime Safety Control for Nonstationary Reinforcement Learning
May 13, 2026Timofey TomashevskiyNon-Stationary RL Constrained RL
McMaster Centre for Software Certification Department of Computing and Software McMaster University, Canada
Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning
May 12, 2026Jingduo Pan, Taoran Wu, Yiling Xue +1Constrained RL Safe RL
Key Laboratory of System Software (Chinese Academy of Sciences), Institute of Software, Chinese Academy of Sciences, Beijing, China · University of Chinese Academy of Sciences, Beijing, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
May 11, 2026Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming +1RL Control Lyapunov-Based Control
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA · Navy Center for Applied Research in Artificial Intelligence (NCARAI), U.S. Naval Research Laboratory, Washington, D.C., USA · Department of Weapons, Robotics, and Control Engineering, United States Naval Academy, Annapolis, MD, USA
Shields to Guarantee Probabilistic Safety in MDPs
May 11, 2026Linus Heck, Filip Macák, Roman Andriushchenko +2Markov Decision Processes Safety-Critical Control
Radboud University, Nijmegen, the Netherlands · Brno University of Technology, Czechia
Robust Probabilistic Shielding for Safe Offline Reinforcement Learning
May 11, 2026Maris F. L. Galesloot, Thomas Rhemrev, Nils JansenOffline RL Safe RL
Radboud University · Nijmegen, The Netherlands · Ruhr University & Radboud University +1
Reinforcement Learning for Scalable and Trustworthy Intelligent Systems
May 8, 2026Guangchen LanFederated RL Privacy-Preserving Language Models
Elmore Family School of Electrical and Computer Engineering, West Lafayette, Indiana · THE PURDUE UNIVERSITY GRADUATE SCHOOL
Learned Lyapunov Shielding for Adaptive Control
May 7, 2026Giansalvo Cirrincione, Adriano FagioliniLyapunov-Based Control Safe RL
Laboratoire LTI, Université de Picardie Jules Verne, Amiens, France. · MIRPALab, Department of Engineering, University of Palermo, 90128 Palermo, Italy.
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
May 6, 2026Dillon Sandhu, Ronald ParrPolicy Optimization Policy Iteration
Department of Computer Science, Duke University, USA
Worst-Case Discovery and Runtime Protection for RL-Based Network Controllers
May 6, 2026Hongyu Hè, Minhao Jin, Maria ApostolakiRL Control Safe RL
Princeton
Constraint-Enhanced Reinforcement Learning Based on Dynamic Decoupled Spherical Radial Squashing
May 5, 2026Qijun Liao, Zhaoxin Yu, Jue YangRobotic Control Constrained RL
School of Mechanical Engineering, University of Science and Technology Beijing · Institute of Automation, Chinese Academy of Sciences
Decoupled Guidance Diffusion for Adaptive Offline Safe Reinforcement Learning
May 4, 2026Rufeng Chen, Zhaofan Zhang, Zhejiang Yang +2Diffusion-Based Planning Diffusion Guidance
1The Hong Kong University of Science and Technology (Guangzhou) · 2Jilin University

May 26, 2026, cs.LGJ/K move · Enter open · S save

Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

Gengyue Han, Yiheng Feng

Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA

Reinforcement Learning Sim-to-Real Transfer Constrained RL Meta-Reinforcement Learning Safe RL

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Due to limited resources and public safety concerns, deep reinforcement learning (RL) agents for many cyber-physical systems (e.g., autonomous vehicles) are first trained in simulators. However, when deployed in real world environments, they often suffer from performance degradation or safety violations because of the inevitable Sim2Real gap. Existing zero-shot approaches, such as robust safe RL and domain randomization, mitigate this issue but typically at the cost of degraded performance or residual safety risks when experiencing unmodeled system dynamics. To address these limitations, we propose a novel reinforcement learning framework that enables safe and efficient policy transfer via probabilistic latent embeddings and dynamic policy adaptation. We consider a family of Constrained Markov Decision Processes (CMDPs) under different environment contexts. By leveraging latent context variable in meta-RL, the proposed framework infers the latent representation of the environment from simulated experiences. Furthermore, it incorporates a distributional RL formulation, which allows risk levels of the deployed policy to be adjusted dynamically, based on the estimation accuracy of the latent context variable. This strategy promotes safety at the early deployment stage and improves efficiency through fast policy adaptation under the Sim2Real gap.

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Latest papers 153